Enhancing Flow Cytometry Discrimination with Geometric Transformation

ABSTRACT

In flow cytometry, particles ( 2 ) can be distinguished between populations ( 8 ) by combining n-dimensional parameter data, which may be derived from signal data from a particle, to mathematically achieve numerical results representative of an alteration ( 48 ). An alteration may include a rotational alteration, a scaled alteration, or perhaps even a translational alteration. Alterations may enhance separation of data points which may provide real-time classification ( 49 ) of signal data corresponding to individual particles into one of at least two populations.

CROSS-REFERENCES TO RELATED APPLICATIONS

This is an international application claiming the benefit of U.S.Provisional Application No. 60/591,957, filed 27 Jul. 2004, herebyincorporated by reference.

TECHNICAL FIELD

The present invention includes in embodiments apparatus and methods forreal-time discrimination of particles while being sorted by flowcytometry. Specifically, embodiments of the invention may includeapplication of various mathematical operations to manipulate data inreal-time resulting in enhanced discrimination between populations ofparticles.

BACKGROUND

One of the most important developments over the last few years has beenthe application of high speed jet-in-air sorters to discriminateparticles and cells that are only subtly different. As but one example,flow cytometry can be used to separate X from Y bearing sperm. While itis in this context that some properties are discussed, it should beunderstood that this is only one example of a broad range ofapplications. Sperm sorting has been applied now to cattle, horses andpigs and may be used with various other animals. In this oneapplication, the intent may be to obtain viable and motile sperm fromsemen or perhaps even be to guaranteed the sex of off-spring that arecreated when a sexed sperm may be inseminated into a female of aspecies. The requirement may be to improve animal husbandry. Forexample, in cattle where dairy is the main product, females may bepreferred or even in beef, the male may be the desirable selection.

Subtle differences can even be quantitative, for example, sperm may notexpress surface antigens that are indicators of the presence of X or Ychromosomes. However, X bearing sperm may have a larger mass of geneticmaterial. A dye, such as Hoechst, may have the property of binding toDNA. As a consequence, light emitted by X bearing sperm, when ignited byultra-violet laser, may be slightly brighter and this can be used as adiscrimination to sort and separate the sperm.

Subtle differences can also be due to flow cytometer geometries as well.In the sperm example, mammalian sperm are generally paddle shaped andwhen they pass through a flow cytometer they can have a randomorientation. This orientation may obscure the differential light comingfrom the X and Y bearing cells. Consequently, a cytometer may have aspecialized orienting nozzle that can use a hydrodynamic effect toorientate the cells to a reasonable degree.

A bivariate histogram may be collected, the parameters may be a forwardand side angle fluorescence. A population may show the effect oforientation. A slight separation of two populations may make a drawingof a closed contour around each population difficult. A contour may beneeded to establish a region that can be exclusively sorted.

DISCLOSURE OF THE INVENTION

Accordingly, it is desirable to provide enhanced discrimination betweenparticles during flow cytometry. An object of the present invention, inembodiments, may include applying mathematical operations to data toallow enhanced discrimination between populations.

Another object of the invention in embodiments may include performing ageometric perhaps 2-dimensional transformation of flow cytometry data sothat particles can be sorted.

It may be another object of the present invention to provide inembodiments an alteration of flow cytometry data in order to distinguishparticles between populations. This may include rotational alterations,translation alterations, scaling alterations and the like in variousembodiments.

Naturally, further objects, goals and embodiments of the inventions aredisclosed throughout other areas of the specification and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a sort overview in accordance with someembodiments of the present invention.

FIG. 2 illustrates forward and side light detection systems inaccordance with some embodiments of the present invention.

FIG. 3 is an example of a histogram of sperm sorting data prior torotation.

FIG. 4 is an example of a histogram of sperm sorting data after scaling.

FIG. 5 is an example of a histogram of sperm sorting data afterrotation.

FIG. 6 is an example of a histogram of sperm sorting data after scalingand rotation.

FIG. 7 is a conceptual depiction of a system for analyzing detectedsignal data in one embodiment.

MODE(S) FOR CARRYING OUT THE INVENTION

The present invention includes a variety of aspects, which may becombined in different ways. The following descriptions are provided tolist elements and describe some of the embodiments of the presentinvention. These elements are listed with initial embodiments, howeverit should be understood that they may be combined in any manner and inany number to create additional embodiments. The variously describedexamples and preferred embodiments should not be construed to limit thepresent invention to only the explicitly described systems, techniques,and applications. Further, this description should be understood tosupport and encompass descriptions and claims of all the variousembodiments, systems, techniques, methods, devices, and applicationswith any number of the disclosed elements, with each element alone, andalso with any and all various permutations and combinations of allelements in this or any subsequent application.

Embodiments of the invention may include various methods, apparatus,systems and the like to distinguish particles during flow cytometry. Afluid stream (1) may be established in a flow cytometer in whichparticles (2) may be entrained. In an embodiment, particles may besperm; however other kinds of particles are certainly possible and allare meant to be included in this disclosure. Particles may be coupledwith a light emitting element, for example one embodiment may include aHoechst dye. Each individual particle may emit a first signal andperhaps even at least one additional signal. A detector (42) may beplaced so as to detect signals from the particles as can be understoodfrom FIGS. 1 and 2. In embodiments, a first signal detector may detect afirst signal affiliated with an individual particle and perhaps even atleast one additional signal detector may detect at least one additionalsignal affiliated with an individual particle. This may include any kindof signal that may be in association, perhaps even close associationwith an individual particle such as but not limited to fluorescence,radiance, and the like.

As an example, FIGS. 1 and 2 represent a flow cytometry sort overview. Anozzle (32) may allow particles (2) entrained in a fluid stream (1) tomove through a laser beam (34) at an interrogation point (33). A signaldetector (42) such as a forward scatter detector (30) and perhaps even aside scatter collection objective (31) may be placed appropriately tocollect signals affiliated to a particle. It may also be desirable toprovide primary laser focusing optics (43). In embodiments, signals mayinclude fluorescence emitted from a light emitting particle coupled withindividual particles after passing through a laser beam. Signals emittedfrom a particle may be passed through optics and perhaps even a pinholestrip (44) to assist in collection of data. A signal detector may beconnected to a system, as discussed hereafter, in which signal dataindicative of the signals may be processed and analyzed in order todetermine a sort decision. Computers, computer programming, hardware,software and the like may assist in a sort decision. While a sortdecision is being determined, particles may pass through a drop delay(35). This time frame may be very short. After a sort decision may havebeen made, a pulse of charge (37) may be applied to a droplet (23)containing a particle. Droplets may pass through charged deflectionplates (38) in order to sort particles into a desired sort receptacle(40) having containers. A waste collection tube (39) may be included inembodiments.

In order to distinguish particles, signal data may be analyzed.Embodiments may include converting signals (e.g. a first signal and atleast one additional signal) affiliated with each individual particleinto n-dimensional parameter data. This may be done with a signalprocessor responsive to the signals. A signal processor may provide aconversion of a first signal and at least one additional signal inton-dimensional parameter data. N-dimensional parameter data may includeone-dimensional or perhaps even multi-dimensional (2-D, 3-D, 4-D, etc.)data which can be associated with each signal detected. Signal data maybe converted to relate each signal with a coordinate, such as anintensity of a color, and the like and may even be plotted in aCartesian coordinate system.

Signals, perhaps even n-dimensional data, may be plotted on a graph(13). Since differences between particles may vary slightly, signalsaffiliated with the particles may also vary slightly. This slightdifference may be so small that when plotted, the n-dimensionalparameter data corresponding to the signals, may place data points veryclose together. These may be so close that it may be difficult tocategorize the data points into a population. Conventional technologiesmay have disregarded these points that are so close together and mayhave decided to throw out that particle because it could not have beendistinguished. It may be desirable in embodiments to reconfigure data toenhance separation between data points.

Embodiments may include distinguishing at least two populations (8) ofn-dimensional parameter data. A population differentiation element mayinclude a geometric transformation and may allow n-dimensional parameterdata to be categorized into one of at least two populations. Forexample, a sperm may be categorized into a X-bearing sperm populationand a Y-bearing population. A geometric two-dimensional transformationmay be performed on flow cytometry data such that particles or evencells may be sorted into separate vials based on an appropriateproperty. In the sperm example, one vial containing cells for male andone vial containing cells for female. A transformation may include amatrix dot product of various translation, scale, and perhaps evenrotation operations for discriminating male determining from femaledetermining cells and may even maintain proper cell type identificationduring signal drift over time. The transformations may be combined intoa single transformation matrix so that the calculations can be performedwithin the available signal processing time such as with a DigitalSignal Processor.

In embodiments, the present invention may include real-time classifyingn-dimensional parameter data of each of the individual particles intoone of at least two populations. A real-time classification element mayinclude classifying signal data into a population, making a sortdecision, and sorting a particle all within the small amount of time ittakes for the particle to move through a flow cytometer. Accordingly,the present invention may provide in embodiments, sorting individualparticles based upon a real-time classification.

The present invention may provide in embodiments, visuallydistinguishing at least two populations of n-dimensional parameter data.This may assist a user to ensure that a sorting may be running properly.This may also allow for user input to assist in discrimination betweenpopulations. Embodiments may include graphically placing n-dimensionalparameter data for each of the individual particles in relation to atleast two populations. For example, it may be desirable to plotn-parameter data in a Cartesian coordinate system. A first signal may beplotted on one axis (14) and at least one additional signal may beplotted on at least one additional axis (15), as can be seen in FIG. 3.In yet other embodiments, a histogram (17) of the n-dimensionalparameter data may be provided.

Certain particles or cells may exhibit signals that can show enhanceddifferences by translation or scaling effects. For example, a maledetermining and female determining sperm cell may not be discriminatedfrom each other using a typical unaltered light signal detected by aflow cytometer. Although a digitized signal may have 12 bits ofresolution, a sorting electronics may have 8 bits of resolution possiblydiscarding the lower 4 bits. A detected light from male determining andfemale determining sperm cells may be so similar that the lower 4 bitsmay be required to discriminate between them. Likewise, due to theirsimilarity, the cells may not effectively be discriminated in histogramdata analysis of the detected parameters possibly because histograms mayalso degrade resolution.

Particles or sperm cells may occupy a small area of a histogram (17).The variation of detected signals of cells or particles, perhaps such asmale determining sperm cells with respect to female determining spermcells may be small; as may be the variation within cell or particles ofthe same type. The total range of variation for both types of cells orparticles may span less than half the total available range. Therefore,the lower bits of resolution may be important for discriminating betweenthe cells or particles and perhaps even the upper bits may not benecessary to identify both types of cells or particles. The inventionmay exploit this characteristic, in embodiments, by translating a centerof the total range of signal variation for both types of cells orparticles to an origin of a Cartesian coordinate system, scaling thedata about the origin effectively increasing the variance between types,then translating back to its original location. As a result, histogramdata may show sufficient variation for discriminating between two typesof cells or particles while even preserving enough information toidentify both types from instrument noise, dead or destroyed cells,other foreign particles, and the like. This has particular applicabilityfor sperm cell sex discrimination.

N-dimensional parameter data may be combined, in embodiments, tomathematically achieve numerical results representative of analteration. An alteration calculation may include any kind ofmanipulation of data. For example, embodiments may provide manipulatingn-dimensional parameter data mathematically to achieve a desired result.This may include a rotational alteration perhaps with a rotationalalteration calculation applied to n-dimensional parameter data. Otherembodiments may include a translation alteration perhaps with atranslational alteration calculation. In yet other embodiments,n-dimensional parameter data may be combined to mathematically achievenumerical results representative of a scaling operation perhaps with ascaled alteration calculation. Of course, other kinds of alterations andcalculations may be used and any alteration may be applied as a singleoperation or perhaps even in combination with others. Classification ofa particle may be based upon numerical results representative of analteration.

In embodiments, and as can be conceptually understood from FIG. 7, asignal (45) associated with a particle may be detected by at least onedetector (45) in which signal data may be sent to a signal processor(47). In a signal processor, signal data may be converted inton-dimensional parameter data to which at least one alteration (48) (e.g.rotational alteration, translation operation, scaling operation, anycombination of these and the like) may be applied to the n-dimensionalparameter data. Based on the alteration, n-dimensional parameter datamay be classified into one of at least two populations providing areal-time classification (49). A particle differentiation decision (50)may be made based upon a real-time classification of the n-dimensionalparameter data to which directions may be sent to a flow cytometer (51)in order to charge and sort the particles.

Certain particles or cells may exhibit signals that can show enhanceddifferences by a rotational alteration of the data. For example, inembodiments, the invention may involve rotating data to increase aseparation of data from male determining cells to female determiningcells. Two sources of light may be detected for each cell. The range oflight intensity from one source from male determining cells may overlapthe same light source range from female determining cells. Employing ascale factor, as described above, may increase the separation of themean distribution of the signal range but may not completely eliminatean overlap.

To eliminate the overlap in this particular example, the invention mayconsider a shape of the signal distribution in two-dimensional analysiswhere signals from perhaps both light sources may be correlated in atwo-dimensional Cartesian coordinate system. Graphically, each of thepopulations such as cell types may have an elliptical shaped population(18) with similar non-orthogonal angles of inclination of a major axisand may even have a similar length of a minor axis. The difference inY-intercept of the ellipse major axis for one cell type to the othertype may be greater than the length of the minor axis. Since the majoraxes may be nearly parallel, it may be desirable to orient theelliptical shaped populations orthogonal to at least one axis. Arotation about a mid-point between the two major axes may orient eachellipse orthogonal to the Y-axis possibly effectively eliminating theY-axis overlap.

Signals such as perhaps light intensity may vary with time, which maypreclude the setting of fixed regions by which sort decisions may bebased. During a long sort, populations may drift due to physical changesto the detectors, absorption or degradation of dye, and even otheruncontrollable conditions. Consequently, sort regions may need to bemonitored and adjusted during the sort. This may be tedious, laborintensive and even prone to error.

One embodiment of the present invention may hold a population in itsoriginal location by employing a scale operation in the transformationmatrix. One possible scale factor could be a ratio of an initial meanvalue of the signal range with respect to the current mean; which mayeffectively scale the signal up or down proportionately to the amount ofdrift from the initial mean.

As previously mentioned, a flow cytometer may be used to generate astream into which the cells or other particulates may be injected. Adetection point may be established which may cause a source of laserlight to strike the cells perhaps causing a dye that is carried ongenomic material to fluoresce. In one application, sperm may beorientated by the hydrodynamics of the injection device, and perhaps thefluorescence level could be proportional to the amount of genomic mass.Thus, a differential between X and Y bearing sperm can be detectable.Fluorescence may be detected by sensors established forward of thedetection point and possibly even to an angle, such as 90 degrees, ofthis point as may be understood in FIGS. 1 and 2. A sensory system canpass a pulse of light to an electrical device which may convert a pulselevel into binary numbers suitable for manipulation by a Digital SignalProcessor (“DSP”).

A signal processor, for example a Digital Signal Processor may containhighly optimized algorithms that can perform a specializationtransformation of at least two signal values. This transformation maycause a resulting population of signal to adopt a form, such as where aseparation of X and Y bearing sperm may be delineated. Clarity ofdelineation may allow each population to be selected more accuratelythan in any other separation system.

The selection of which population is to be sorted can be made by anelectronic system such as by controlling a droplet break-off of astandard flow cytometer. There may be a delay between a detection of afluorescence and a particle falling into a last breaking drop (36). Thistime may be known. Thus an electronic system can apply an electriccharge to the drop containing the particle of interest. High voltageplates below the breaking drops may cause the drop to move and be placedinto a collection vial. In this fashion, a high level of selectedparticles, perhaps such as pure sperm cells can be collected as may beunderstood in FIG. 1.

A Digital Signal Processor may control the position of the populations.Flow cytometers may have variability in the pointing accuracy of a laserbeam which can cause an intensity of fluorescence from each cell toshift. A DSP may act as a sensory device to monitor a shifting of lightintensity and may even perform a correcting scale to ensure that thepopulations residing in the fluorescence bivariate remain in the sameposition. This may ensure that the correct sperm are sorted. The processof transformation, zooming and perhaps even control may be imbedded inone geometric transform. Multiple transformation and zoom could beincorporated perhaps even with non-linear, logarithmic, table look up,or even discretely unique data portion transformations. Transformationmay uniquely supply the accuracy required to provide highly pureparticle separation.

One method of gaining spatially separated data may be to usecompensation algorithms as those skilled in the art could appreciate.Rotation of the data (forward scatter vs. side scatter) may be a moreaccurate mechanism to do this. In addition to rotation, it has beenfound that there may be a need for tracking and zooming of the data.These combinations can be significant, for example sex selection may notbe optimally achieved by any other method, hence the importance of thisinvention.

By rotating a bivariate histogram in which two populations may bepresent but overlapping in one dimension, a better spatial separation inthe dimension that is overlapped may be created. Rotated data can beused as a parameter in sort decisions and perhaps even in any otherhistograms. If data has been properly rotated, a univariate histogram ofthe parameter of interest can contain gaps in the populations. Inembodiments, a rotation function may be utilized to set the X and Ypopulations on a bivariate to be horizontal.

Because the populations of interest may typically be close together, itmay be desirable—either alone or in combination with other aspects—tozoom in on a region in order to exaggerate a distinction between thepopulations. This may be another mechanism to allow separation of thepopulations. Further, the long sorts typical of sperm sorting or thelike may cause data to shift over time. Shifts may be fixed by setting atracking region and using this region in a newly computed parameter. Inembodiments, an automatic region setting algorithm may be implemented.In other embodiments, regions may be set according to a desired puritylevel.

In order to rotate and sort data, a rotation could be done usinghardware that can access the data in an acquisition rack and may have acapability to modify an event frame prior to a sort decision being made.Rather than design new hardware, it may be desirable to implement thisin a DSP perhaps by using a rotation algorithm. This may include anability to do compensation on data when rotating it. In addition torotation, a user can specify a region to zoom in on.

In embodiments, the present invention may provide specification of acenter point of a rotation. This may allow a finer control of therotation in order to achieve maximum separation.

Embodiments of the present invention may include providing an-dimensional space alteration function having at least:

a first 1st-dimensional alteration value, a second 1st-dimensionalalteration value, and a third 1st-dimensional alteration value;

a first 2nd-dimensional alteration value, a second 2nd-dimensionalalteration value, and a third 2nd-dimensional alteration value, and thelike. This may be representative of a matrix, as one skilled in the artcan appreciate and can be understood by the various examples givenherein.

N-dimensional space alteration functions may be combined with a vectorhaving data points so as to alter the n-dimensional parameter data. Inan embodiment, a combination of a function and data points may include,but is not limited to: calculating a first 1st-dimensional alterationvalue times a 1st-dimensional data point summed with a second1st-dimensional alteration value times a 2nd-dimensional data pointsummed with a third 1st-dimensional alteration value times a3rd-dimensional data point to thereby create a first dimensional altereddata point; and calculating a first 2nd-dimensional alteration valuetimes a 1st-dimensional data point summed with a second 2nd-dimensionalrotational value times a 2nd-dimensional data point summed with a third2nd-dimensional alteration value times a 3rd-dimensional data point tothereby create a second dimensional altered data point.

In other embodiments, a n-dimensional space alteration function mayinclude a first 3rd-dimensional alteration value, a second3rd-dimensional alteration value and a third 3rd-dimensional alterationvalue. Further, a combination of data may include calculating a first3rd-dimensional alteration value times a 1st-dimensional data pointsummed with a second 3rd-dimensional rotational value times a2nd-dimensional data point summed with a third 3rd-dimensionalalteration value times a 3rd-dimensional data point to thereby create athird dimensional altered data point.

Many different types of data manipulation values that may be used as analteration value. For example, a rotational alteration may includealteration values based upon an angle of rotation allowing altered datapoints to increase discrimination of at least two populations ofn-dimensional data. In other embodiments, a scaled alteration mayinclude alteration values are based upon a zoom and tracking elementallowing altered data points to increase discrimination of at least twopopulations of n-dimensional data. In yet other embodiments, atranslation alteration may include alteration values which may translaten-parameter data with respect to a center point of rotation.

The following are examples of matrices that may be used in order to dorotation about a given center point and even zoom or tracking on aregion and the like.

Translate data:

${T\left( {C_{x},C_{y}} \right)} = \begin{pmatrix}1 & 0 & {Cx} \\0 & 1 & {Cy} \\0 & 0 & 1\end{pmatrix}$

Rotate the data:

${R\; \theta} = \begin{pmatrix}{\cos \; \theta} & {\sin \; \theta} & 0 \\{{- \sin}\; \theta} & {\cos \; \theta} & 0 \\0 & 0 & 1\end{pmatrix}$

Scale the data (Zoom and Tracking):

${S\left( {D_{x},D_{y}} \right)} = \begin{pmatrix}{Dx} & 0 & 0 \\0 & {Dy} & 0 \\0 & 0 & 1\end{pmatrix}$

These can be combined with the variables:

Res=Resolution/2

Z=Zoom amount

RC=Rotation Center

Tr=Tracking constant

ZC=Tracking center

Θ=Angle of rotation

into a single transformation produced by the product of any or all ofthe following linear operations:

-   -   T(Res_(x), Res_(y))    -   S(Z_(x), Z_(y))    -   T(−ZC_(x), −ZC_(y))    -   S(Tr_(x), Tr_(y))    -   T(RC_(x), RC_(y))    -   Rθ    -   T(−RC_(x), −RC_(y))

The equation, T(−RC_(x), −RC_(y)), may translate an event to an originand then the opposite in order to translate it back to a propercoordinate system. This may be done with all listmode data in the firstquadrant.

When zooming, the data that is being zoomed may be centered in ahistogram. This may be why that data could be translated to half (½) theresolution rather than back to a center point of a zoomed region.

Other examples of translation alterations may be based upon an operationsuch as rotation in x-axis, rotation in y-axis, rotation in z-axis,translation, scale, perspective, higher order and the like operations.Some examples include the following 3-D algorithms:

Rotation in X-Axis:

${R_{X}(\theta)} = {\begin{matrix}1 & {0\mspace{50mu}} & {0\mspace{56mu}} & 0 \\0 & {\cos (\theta)} & {- {\sin (\theta)}} & 0 \\0 & {\sin (\theta)} & {\cos (\theta)} & 0 \\0 & {0\mspace{50mu}} & {0\mspace{56mu}} & 1\end{matrix}}$

Rotation in Y-Axis:

${R_{Y}(\theta)} = {\begin{matrix}{{\cos (\theta)}\mspace{14mu}} & 0 & {\sin (\theta)} & 0 \\{0\mspace{65mu}} & 1 & {0\mspace{50mu}} & 0 \\{- {\sin (\theta)}} & 0 & {\cos (\theta)} & 0 \\{\; 0\mspace{70mu}} & 0 & {0\mspace{45mu}} & 1\end{matrix}}$

Rotation in Z-Axis:

${R_{Z}(\theta)} = {\begin{matrix}{\cos (\theta)} & {- {\sin (\theta)}} & 0 & 0 \\{\sin (\theta)} & {\cos (\theta)} & 0 & 0 \\{0\mspace{45mu}} & 0 & 1 & 0 \\{0\mspace{45mu}} & 0 & 0 & 1\end{matrix}}$

Translation:

${T\left( {T_{X},T_{Y},T_{Z}} \right)} = {\begin{matrix}1 & 0 & 0 & T_{X} \\0 & 1 & 0 & T_{Y} \\0 & 0 & 1 & T_{Z} \\0 & 0 & 0 & {\mspace{11mu} 1\;}\end{matrix}}$

Scale:

${S\left( {S_{X},S_{Y},S_{Z}} \right)} = {\begin{matrix}S_{X} & 0 & 0 & 0 \\{0\mspace{14mu}} & S_{Y} & 0 & 0 \\{0\mspace{14mu}} & 0 & S_{Z} & 0 \\{\; 0\mspace{20mu}} & 0 & 0 & 1\end{matrix}}$

Of course other algorithms may be used and all are meant to be includedin this disclosure. The examples provided herein are not meant to belimiting.

Programs may solve the particular equation used and may download theresult to the DSP. A DSP code may perform a simple matrix multiplicationusing the matrices supplied by computer programs. Further, a computerprogram may create zoom parameters such that a mean of the zoomedsignals may remain fixed. The mean may remain at the same positionwithin a tracking region. Software may allow a user to designate regionsbounds which a zoom function may utilize. Software may send a center ofa zoom, zoom value and even tracking gain to a DSP.

In embodiments, zooming of a square region on a split of populations(such as X and Y populations) may be shown on a Forward-Fluorescenceversus a Side-fluorescence bivariate histogram. A zoom region may be asquare or perhaps even non-square with a maximum size which may be equalto a size of a bivariate. In embodiments, it may be desirable to use again set at 1 and offset at 0 on both parameters. A zoom region can beset as a fraction in percentage of the bivariate range in linear units.As those skilled in the art would recognize, 1/16 may offer a digitaladvantage. In embodiments, software gain (distinct from a zoom gain) canbe altered. Because the DSP may be a fixed point processor, a highlyoptimized assembly code may be used to do the floating point math.

In order to achieve efficient calculations of data within the sort timeperiod of real time classifying, the present invention in embodimentsmay include simultaneously processing two or more alterationcalculations. This may include simultaneously processing n-dimensionalparameter data to mathematically achieve numerical results of both arotational alteration and a scaled alteration. Other embodiments mayinclude simultaneously processing n-dimensional parameter data tomathematically achieve numerical results of a rotational alteration, ascaled alteration and perhaps even a translation alteration. Of course,additional alterations may be simultaneously or perhaps evensequentially processed as well.

FIG. 3 may be an image of data prior to rotation. FIG. 4 shows an imageof data after scaling, FIG. 5 show an image of data after rotation andFIG. 6 shows an image of data after scaling and rotation. These mayinclude one type of illustrations of data that can be expected. It isalso common to see these illustrations of data from sperm sorting data.While rotating may allow a user to gain spatially separated data, it maydistort the relative values of intensity and in embodiments, may not beused for any reason other than gaining the space between distinctpopulations. It may be possible to remove the values on the axis of therotated data in order to assure that users have a visual way ofrecognizing this.

As can be easily understood from the foregoing, the basic concepts ofthe present invention may be embodied in a variety of ways. It involvesboth transformation techniques as well as devices to accomplish theappropriate transformation. In this application, the transformationtechniques are disclosed as part of the results shown to be achieved bythe various devices described and as steps which are inherent toutilization. They are simply the natural result of utilizing the devicesas intended and described. In addition, while some devices aredisclosed, it should be understood that these not only accomplishcertain methods but also can be varied in a number of ways. Importantly,as to all of the foregoing, all of these facets should be understood tobe encompassed by this disclosure.

The discussion included in this application is intended to serve as abasic description. The reader should be aware that the specificdiscussion may not explicitly describe all embodiments possible; manyalternatives are implicit. It also may not fully explain the genericnature of the invention and may not explicitly show how each feature orelement can actually be representative of a broader function or of agreat variety of alternative or equivalent elements. Again, these areimplicitly included in this disclosure. Where the invention is describedin device-oriented terminology, each element of the device implicitlyperforms a function. Apparatus claims may not only be included for thedevice described, but also method or process claims may be included toaddress the functions the invention and each element performs. Neitherthe description nor the terminology is intended to limit the scope ofthe claims that will be included in any subsequent patent application.

It should also be understood that a variety of changes may be madewithout departing from the essence of the invention. Such changes arealso implicitly included in the description. They still fall within thescope of this invention. A broad disclosure encompassing both theexplicit embodiment(s) shown, the great variety of implicit alternativeembodiments, and the broad methods or processes and the like areencompassed by this disclosure and may be relied upon when drafting theclaims for any subsequent patent application. It should be understoodthat such language changes and broader or more detailed claiming may beaccomplished at a later date. With this understanding, the reader shouldbe aware that this disclosure is to be understood to support anysubsequently filed patent application that may seek examination of asbroad a base of claims as deemed within the applicant's right and may bedesigned to yield a patent covering numerous aspects of the inventionboth independently and as an overall system.

Further, each of the various elements of the invention and claims mayalso be achieved in a variety of manners. Additionally, when used orimplied, an element is to be understood as encompassing individual aswell as plural structures that may or may not be physically connected.This disclosure should be understood to encompass each such variation,be it a variation of an embodiment of any apparatus embodiment, a methodor process embodiment, or even merely a variation of any element ofthese. Particularly, it should be understood that as the disclosurerelates to elements of the invention, the words for each element may beexpressed by equivalent apparatus terms or method terms—even if only thefunction or result is the same. Such equivalent, broader, or even moregeneric terms should be considered to be encompassed in the descriptionof each element or action. Such terms can be substituted where desiredto make explicit the implicitly broad coverage to which this inventionis entitled. As but one example, it should be understood that allactions may be expressed as a means for taking that action or as anelement which causes that action. Similarly, each physical elementdisclosed should be understood to encompass a disclosure of the actionwhich that physical element facilitates. Regarding this last aspect, asbut one example, the disclosure of a “detector” should be understood toencompass disclosure of the act of “detecting”—whether explicitlydiscussed or not—and, conversely, were there effectively disclosure ofthe act of “detecting”, such a disclosure should be understood toencompass disclosure of a “detector” and even a “means for detecting.”Such changes and alternative terms are to be understood to be explicitlyincluded in the description.

Any patents, publications, or other references mentioned in thisapplication for patent are hereby incorporated by reference. Allpriority cases are also incorporated by reference. In addition, as toeach term used it should be understood that unless its utilization inthis application is inconsistent with such interpretation, commondictionary definitions should be understood as incorporated for eachterm and all definitions, alternative terms, and synonyms such ascontained in the Random House Webster's Unabridged Dictionary, secondedition are hereby incorporated by reference. Finally, all referenceslisted in the list of references below or other information statementfiled with the application are hereby appended and hereby incorporatedby reference, however, as to each of the above, to the extent that suchinformation or statements incorporated by reference might be consideredinconsistent with the patenting of this/these invention(s) suchstatements are expressly not to be considered as made by theapplicant(s).

I. U.S. PATENT DOCUMENTS DOCUMENT NO. & KIND PUB'N DATE PATENTEE OR CODE(if known) mm-dd-yyyy APPLICANT NAME 3,299,354 12/17/67 Hogg 3,661,46005/09/72 Elking et al. 3,710,933 01/16/73 Fulwyler et al 3,761,94109/25/73 Robertson 3,810,010 05/07/74 Thom 3,826,364 07/30/74 Bonner etal 3,833,796 11/03/74 Fetner et al 3,960,449 07/01/76 Carleton et al3,963,606 06/15/76 Hogg 3,973,196 08/03/76 Hogg 4,014,611 03/29/77Simpson et al 4,070,617 01/24/78 Kachel et al 4,074,809 2/21/2978McMillin et al. 4,162,282 07/24/79 Fulwyler et al 4,230,558 10/28/80Fulwyler 4,302,166 11/24/81 Fulwyler et al 4,317,520 03/02/82 Lombardoet al 4,318,480 03/09/82 Lombardo et al 4,318,481 03/09/82 Lombardo etal 4,318,482 03/09/82 Barry et al 4,318,483 03/09/82 Lombardo et al4,325,483 04/20/82 Lombardo et al 4,341,471 07/27/82 Hogg et al4,350,410 09/21/82 Minott 4,361,400 11/30/82 Gray et al 4,395,67607/26/83 Hollinger et al 4,400,764 08/23/83 Kenyon 4,487,320 12/11/84Auer 4,498,766 02/12/85 Unterleitner 4,501,336 02/26/1985 Kemp et al.4,515,274 05/07/85 Hollinger et al 4,523,809 06/18/85 Toboada et al4,538,733 11/03/85 Hoffman 4,598,408 07/01/86 O'Keefe 4,600,302 07/15/86Sage, Jr. 4,631,483 12/23/86 Proni et al 4,673,288 06/16/87 Thomas et al4,691,829 09/08/87 Auer 4,702,598 10/27/87 Böhmer 4,744,090 05/10/88Freiberg 4,758,729 07/19/88 Monnin 4,794,086 01/27/88 Kasper et al4,818,103 04/04/89 Thomas et al 4,831,385 05/16/89 Archer et al4,845,025 07/04/89 Lary et al 4,877,965 10/31/89 Dandliker et al4,942,305 07/17/90 Sommer 4,981,580 01/01/91 Auer 4,983,038 01/08/91Ohki et al 4,987,539 01/22/91 Moore, et al. 5,005,981 04/09/91 Schulteet al 5,007,732 04/16/91 Ohki et al 5,030,002 07/09/91 North, Jr.5,034,613 07/23/91 Denk et al 5,079,959 01/14/92 Miyake et al 5,098,65703/24/92 Blackford et al 5,101,978 04/07/92 Marcus 5,199,576 04/06/93Corio, et al. 5,127,729 07/07/92 Oetliker et al 5,135,759 08/04/1992Johnson 5,144,224 09/01/92 Larsen 5,150,313 09/22/92 Van den Engh et al5,159,397 10/27/92 Kosaka et al 5,159,403 10/27/92 Kosaka 5,167,92612/01/92 Kimura et al 5,180,065 01/19/93 Touge et al 5,182,617 01/26/93Yoneyama et al 5,199,576 04/06/93 Corio et al 5,215,376 06/01/93 Schulteet al 5,247,339 09/21/93 Ogino 5,259,593 11/09/93 Orme et al 5,260,76411/09/93 Fukuda et al 5,298,967 03/29/94 Wells 5,359,907 11/01/94 Bakeret al 5,367,474 11/22/94 Auer, et al. 5,370,842 12/06/94 Miyazaki et al5,412,466 05/02/95 Ogino 5,452,054 09/19/95 Dewa et al 5,466,57211/14/95 Sasaki, et al 5,467,189 11/14/95 Kreikebaum et al 5,471,29411/28/1995 Ogino 5,483,469 01/09/96 Van den Engh et al 5,503,99404/02/1996 Shear et al. 5,523,573 06-04-96 Hänninen et al 5,558,99809/24/96 Hammond, et al 5,596,401 01/21/97 Kusuzawa 5,601,235 02/11/97Booker et al 5,602,039 02/11/97 Van den Engh 5,602,349 02/11/97 Van denEngh 5,641,457 07/24/97 Vardanega, et al 5,643,796 07/01/97 Van den Enghet al 5,650,847 07/22/97 Maltsev et al 5,672,880 09/30/97 Kain 5,675,40110/07/97 Wangler et al 5,700,692 12/23/97 Sweet 5,707,808 01/13/98Roslaniec et al 5,726,364 03/10/98 Van Den Engh 5,759,767 06/02/98Lakowicz et al 5,777,732 06/07/98 Hanninen et al 5,786,560 07/28/98Tatah et al 5,796,112 08/18/98 Ichie 5,815,262 09/29/98 Schrof et al5,824,269 10/20/1998 Kosaka et al. 5,835,262 11/10/98 Iketaki et al5,880,457 03/09/1999 Tomiyama et al. 5,912,257 06/15/99 Prasad et al5,916,449 06/29/1999 Ellwart et al. 6,589,792 07/08/2003 Malachowski6,248,590 06/19/2001 Malachowski 4,361,400 11/30/1982 Gray et al.

II. FOREIGN PATENT DOCUMENTS Foreign Patent Document Country Code,Number, PUB'N DATE Kind Code (if known) mm-dd-yyyy PATENTEE OR APPLICANTNAME DE19549015 03-04-97 Ellwart et al. EP 0781985 A2 07-02-97 Karls etal. EP0160201A2 11/06/85 Sage et al. EP025296A2 03/18/81 Lombardo et al.EP0468100A1 01/29/92 Kosaka, Tokihiro EP0786079 B1 05/21/2003 Van DenEngh FR2699678-A1 12/23/92 MERLU BENOIT JP 4-126066 04/27/92 SAKAMOTOKAZUCHIKA, et al. JP 4-126081 04/27/92 SAKAMOTO KAZUCHIKA, et al.JP2024535 01/26/90 MIYAMOTO MORITOSHI, et al. JP4126064 (A) 04/27/92SHIOMI ATSUSHI, et al. JP4126066 (A) 04/27/92 SAKAMOTO KAZUCHIKA, et al.JP4126081 (A) 04/27/92 SAKAMOTO KAZUCHIKA et al. JP61139747 (A) 06/27/86ITO YUJI JP61159135 (A) 07/18/86 ITO YUJI SU1056008 11/23/83 TRETYAKOVALEKSANDR et al. SU1260778-A1 09/30/86 YAGUNOV ALEKSEJ et al WO96/12172A1 04/25/1996 Van Den Engh WO 99/44037 02/26/99 Malachowski WO01/28700A1 04/26/2001 Ellison, et al.

III. OTHER DOCUMENTS Axicon; Journal of the Optical Society of America;Vol. 44, #8, Eastman Kodak Company, Hawk-Eye Works, Rochester, NY, Sep.10, 1953, pp. 592-597 Ceruzzi, P., “History of Modern Computing”, MITPress, Reference to Non-von Neumann. Denk, W., et al (1995). Two-photonmolecular excitation in laser scanning microscopy. Handbook ofBiological Conical Microscopy. J. B. Pawley, ed., Plenum Press, NewYork, pp 444-458. Garner, D. L. et al; “Quantification of the X- and Y-Chromosome-Bearing Spermatozoa of Domestic Animals by Flow Cytometry¹,Biology of Reproduction 28, pgs. 312-321, (1983) Goppert-Mayer, M.1931,. Über Elementarakte mit zwei Quantensprüngen nnalen der Physik,Pages 273-294 Johnson, Lawrence A. “Sex Preselection by Flow CytometricSeparation of X and Y Chromosome-bearing Sperm based on NA Difference: aReview, Reprod. Fertil. Dev., 1995, 7, pgs. 893-903 Manni, Jeff; (1996).Two-Photon Excitation Expands The Capabilities of Laser-ScanningMicroscopy, Biophotonics International, pp 44-52 Melamed et al, AnHistorical Review of the Development of Flow Cytometers and Sorters,,1979, pp. 3-9 Piston. D. W.. et al (1994). Two-photon-excitationfluorescence imaging of three-dimensional calcium ion activity. APPLIEDOPTICS 33: 662-669 Piston, D. W., et al. (1995). Three-dimensionallyresolved NAD(P)H cellular metabolic redox imaging of the in-situ corneawith two-photon excitation laser scanning microscopy. J OF MICROSCOPY178: 20-27 Radbruch, A Flow Cytometry and Cell Sorting,. (Ed.),“Operation of a Flow Cytometer” by Gottlinger et al., 1992, p. 7-23Sharpe, J., Thesis: “An Introduction to Flow Cytometry,” pp 5-7 and pp33-42 and page 55. Shapiro, H. M.D., “Practical Flow Cytometry”, ThirdEdition, John Wiley & Sons, Inc., Publication. Skogen-Hagenson, M. J. etal; “A High Efficiency Flow Cytometer,” The Journal of Histochemistryand Cytochemistry, Vol. 25, No. 7, pp. 784-789, 1977, USA Van Dilla etal. (Eds.), Flow Cytometry: Instrumentation and Data Analysis, “FlowChambers and Sample Handling,” by Pinkel et al., 1985, pp. 77-128 VanDilla et al. (Eds.)Flow Cytometry: Instrumentation and Data Analysis,,“Overview of Flow Cytometry: Instrumentation and Data Analysis” byMartin Van Dilla, 1985, pp. 1-8 Williams, R. M. et al. (1944). Twophoton molecular excitation provides intrinsic 3-dimensional resolutionfor laser- based microscopy and microphotochemistry. FASEB J. 8:804-813. “An Introduction to Flow Cytometry”, pp 5-7 and pp 33-42 andpage 55. Gottlinger, C., et al, “Operation of a Flow Cytometer,” FlowCytometry and Cell Sorting, pp. 7-23 (1982) McLeod, J., Eastman KodakCompany, Hawk-Eye Works, Rochester, NY, Journal of the Optical Societyof America; vol. 44, no. 8, September 1953, pp. 592-597 Pinkel, D.,“Flow Chambers and Sample Handling,” Flow Cytometry: Instrumentation andData Analysis, pp. 77-128 (1985) U.S. application 09/032,733, entitled“Method and Apparatus for Flow Cytometry,” filed on Feb. 27, 1998, 53pages and 5 figures U.S. application No. 60/591,957, entitled,“Geometric Transformation for Enhanced Flow Cytometry Discrimination,”filed Jul. 7, 2004, 19 pages.

Thus, the applicant(s) should be understood to have support to claim andmake a statement of invention to at least: i) each of the transformationdevices as herein disclosed and described, ii) the related methodsdisclosed and described, iii) similar, equivalent, and even implicitvariations of each of these devices and methods, iv) those alternativedesigns which accomplish each of the functions shown as are disclosedand described, v) those alternative designs and methods which accomplisheach of the functions shown as are implicit to accomplish that which isdisclosed and described, vi) each feature, component, and step shown asseparate and independent inventions, vii) the applications enhanced bythe various systems or components disclosed, viii) the resultingproducts produced by such systems or components, ix) each system,method, and element shown or described as now applied to any specificfield or devices mentioned, x) methods and apparatuses substantially asdescribed hereinbefore and with reference to any of the accompanyingexamples, xi) the various combinations and permutations of each of theelements disclosed, xii) each potentially dependent claim or concept asa dependency on each and every one of the independent claims or conceptspresented.

In addition and as to computer aspects and each aspect amenable toprogramming or other electronic automation, the applicant(s) should beunderstood to have support to claim and make a statement of invention toat least: xiii) processes performed with the aid of or on a computer asdescribed throughout the above discussion, xiv) a programmable apparatusas described throughout the above discussion, xv) a computer readablememory encoded with data to direct a computer comprising means orelements which function as described throughout the above discussion,xvi) a computer configured as herein disclosed and described, xvii)individual or combined subroutines and programs as herein disclosed anddescribed, xviii) the related methods disclosed and described, xix)similar, equivalent, and even implicit variations of each of thesesystems and methods, xx) those alternative designs which accomplish eachof the functions shown as are disclosed and described, xxi) thosealternative designs and methods which accomplish each of the functionsshown as are implicit to accomplish that which is disclosed anddescribed, xxii) each feature, component, and step shown as separate andindependent inventions, and xxiii) the various combinations andpermutations of each of the above.

With regard to claims whether now or later presented for examination, itshould be understood that for practical reasons and so as to avoid greatexpansion of the examination burden, the applicant may at any timepresent only initial claims or perhaps only initial claims with onlyinitial dependencies. Support should be understood to exist to thedegree required under new matter laws—including but not limited toEuropean Patent Convention Article 123(2) and United States Patent Law35 U.S.C. § 132 or other such laws—to permit the addition of any of thevarious dependencies or other elements presented under one independentclaim or concept as dependencies or elements under any other independentclaim or concept. In drafting any claims at any time whether in thisapplication or in any subsequent application, it should also beunderstood that the applicant has intended to capture as full and broada scope of coverage as legally available. To the extent thatinsubstantial substitutes are made, to the extent that the applicant didnot in fact draft any claim so as to literally encompass any particularembodiment, and to the extent otherwise applicable, the applicant shouldnot be understood to have in any way intended to or actuallyrelinquished such coverage as the applicant simply may not have beenable to anticipate all eventualities; one skilled in the art, should notbe reasonably expected to have drafted a claim that would have literallyencompassed such alternative embodiments.

Further, if or when used, the use of the transitional phrase“comprising” is used to maintain the “open-end” claims herein, accordingto traditional claim interpretation. Thus, unless the context requiresotherwise, it should be understood that the term “comprise” orvariations such as “comprises” or “comprising”, are intended to implythe inclusion of a stated element or step or group of elements or stepsbut not the exclusion of any other element or step or group of elementsor steps. Such terms should be interpreted in their most expansive formso as to afford the applicant the broadest coverage legally permissible.

Finally, any claims set forth at any time are hereby incorporated byreference as part of this description of the invention, and theapplicant expressly reserves the right to use all of or a portion ofsuch incorporated content of such claims as additional description tosupport any of or all of the claims or any element or component thereof,and the applicant further expressly reserves the right to move anyportion of or all of the incorporated content of such claims or anyelement or component thereof from the description into the claims orvice-versa as necessary to define the matter for which protection issought by this application or by any subsequent continuation, division,or continuation-in-part application thereof, or to obtain any benefitof, reduction in fees pursuant to, or to comply with the patent laws,rules, or regulations of any country or treaty, and such contentincorporated by reference shall survive during the entire pendency ofthis application including any subsequent continuation, division, orcontinuation-in-part application thereof or any reissue or extensionthereon.

1. A method of flow cytometry comprising the steps of: establishing afluid stream; entraining particles in said fluid stream; detecting afirst signal affiliated with individual particles in said fluid stream;detecting at least one additional signal affiliated with said individualparticles; converting said first signal and said at least one additionalsignal affiliated with said individual particles into n-dimensionalparameter data; combining said n-dimensional parameter data tomathematically achieve numerical results representative of a rotationalalteration; distinguishing at least two populations of saidn-dimensional parameter data; real-time classifying said n-dimensionalparameter data of each of said individual particles into one of said atleast two populations based upon said numerical results representativeof said rotational alteration; and sorting said individual particlesbased upon said real-time classification.
 2. A method of flow cytometryaccording to claim 1 wherein said step of combining said n-dimensionalparameter data to mathematically achieve numerical resultsrepresentative of a rotational alteration comprises the steps of:providing a n-dimensional space alteration function having at least: afirst 1st-dimensional alteration value, a second 1st-dimensionalalteration value, and a third 1st-dimensional alteration value; and afirst 2nd-dimensional alteration value, a second 2nd-dimensionalalteration value, and a third 2nd-dimensional alteration value;calculating said first 1st-dimensional alteration value times a1st-dimensional data point summed with said second 1st-dimensionalalteration value times a 2nd-dimensional data point summed with saidthird 1st-dimensional alteration value times a 3rd-dimensional datapoint to thereby create a first dimensional altered data point; andcalculating said first 2nd-dimensional alteration value times said 1st-dimensional data point summed with said second 2nd-dimensionalalteration value times said 2nd-dimensional data point summed with saidthird 2nd-dimensional alteration value times said 3rd-dimensional datapoint to thereby create a second dimensional altered data point.
 3. Amethod of flow cytometry according to claim 2 wherein said alterationvalues are based upon an angle of rotation allowing said altered datapoints to increase discrimination of said at least two populations ofsaid n-dimensional data.
 4. A method of flow cytometry according toclaim 1 and further comprising the steps of: combining saidn-dimensional parameter data to mathematically achieve numerical resultsrepresentative of a scaled alteration; and real-time classifying saidn-dimensional parameter data of each of said individual particles intoone of said at least two populations based upon said numerical resultsrepresentative of said scaled alteration.
 5. A method of flow cytometryaccording to claim 4 wherein said step of combining said n-dimensionalparameter data to mathematically achieve numerical resultsrepresentative of said scaled alteration comprises the steps of:providing a n-dimensional space alteration function having at least: afirst 1st-dimensional alteration value, a second 1st-dimensionalalteration value, and a third 1st-dimensional alteration value; and afirst 2nd-dimensional alteration value, a second 2nd-dimensionalalteration value, and a third 2nd-dimensional alteration value;calculating said first 1st-dimensional alteration value times a1st-dimensional data point summed with said second 1st-dimensionalalteration value times a 2nd-dimensional data point summed with saidthird 1st-dimensional alteration value times a 3rd-dimensional datapoint to thereby create a first dimensional altered data point; andcalculating said first 2nd-dimensional alteration value times said1st-dimensional data point summed with said second 2nd-dimensionalalteration value times said 2nd-dimensional data point summed with saidthird 2nd-dimensional alteration value times said 3rd-dimensional datapoint to thereby create a second dimensional altered data point.
 6. Amethod of flow cytometry according to claim 5 wherein said alterationvalues are based upon a zoom and tracking element allowing said altereddata points to increase discrimination of said at least two populationsof said n-dimensional data.
 7. A method of flow cytometry according toclaim 4 and further comprising the step of simultaneously processingsaid n-dimensional parameter data to mathematically achieve numericalresults of both said rotational alteration and said scaled alteration.8. A method of flow cytometry according to claim 1 or 4 and furthercomprising the steps of: combining said n-dimensional parameter data tomathematically achieve numerical results representative of atranslational alteration; and real-time classifying said n-dimensionalparameter data of each of said individual particles into one of said atleast two populations based upon said numerical results representativeof said translational alteration.
 9. A method of flow cytometryaccording to claim 8 wherein said step of combining said n-dimensionalparameter data to mathematically achieve numerical resultsrepresentative of a translational alteration comprises the steps of:providing a n-dimensional space alteration function having at least: afirst 1st-dimensional alteration value, a second 1st-dimensionalalteration value, and a third 1st-dimensional alteration value; and afirst 2nd-dimensional alteration value, a second 2nd-dimensionalalteration value, and a third 2nd-dimensional alteration value;calculating said first 1st-dimensional alteration value times a1st-dimensional data point summed with said second 1st-dimensionalalteration value times a 2nd-dimensional data point summed with saidthird 1st-dimensional alteration value times a 3rd-dimensional datapoint to thereby create a first dimensional altered data point; andcalculating said first 2nd-dimensional alteration value times said 1st-dimensional data point summed with said second 2nd-dimensionalalteration value times said 2nd-dimensional data point summed with saidthird 2nd-dimensional alteration value times said 3rd-dimensional datapoint to thereby create a second dimensional altered data point.
 10. Amethod of flow cytometry according to claim 9 wherein said alterationvalues comprise translating of said n-parameter data with respect to acenter point of rotation.
 11. A method of flow cytometry according toclaim 9 wherein said space alteration function is based upon anoperation selected from a group consisting of rotation in x-axis,rotation in y-axis, rotation in z-axis, translation, scale, andperspective operations.
 12. A method of flow cytometry according toclaim 8 and further comprising the step of simultaneously processingsaid n-dimensional parameter data to mathematically achieve numericalresults of both said rotational alteration, said scaled alteration andsaid translation alteration.
 13. A method of flow cytometry according toclaim 1 wherein said step of distinguishing said at least twopopulations of said n-dimensional parameter data comprises the step ofvisually distinguishing said at least two populations of saidn-dimensional parameter data.
 14. A method of flow cytometry accordingto claim 13 wherein said step of visually distinguishing said at leasttwo populations of said n-dimensional parameter data comprisesgraphically placing said n-dimensional parameter data for each of saidindividual particles in relation to said at least two populations.
 15. Amethod of flow cytometry according to claim 13 wherein said step ofvisually distinguishing said at least two populations of saidn-dimensional parameter data comprises plotting said n-parameter data ina Cartesian coordinate system.
 16. A method of flow cytometry accordingto claim 15 wherein said step of plotting said n-parameter data in aCartesian coordinate system comprises the step of plotting said firstsignal on one axis and plotting said at least one additional signal onat least one additional axis.
 17. A method of flow cytometry accordingto claim 13 wherein said step of visually distinguishing said at leasttwo populations of said n-dimensional parameter data comprises the stepof providing a histogram of said n-dimensional parameter data.
 18. Amethod of flow cytometry according to claim 1 wherein said step ofdistinguishing said at least two populations of said n-dimensionalparameter data comprises the steps of: graphically providing ellipticalshaped populations of each of said at least two populations having anon-orthogonal angles of inclination to at least one axis; and orientingsaid elliptical shaped populations orthogonal to at least one axis. 19.A method of flow cytometry according to claim 1 wherein said particlescomprises sperm; and wherein said at least two populations comprises anX-bearing sperm population and a Y-bearing sperm population.
 20. Amethod of flow cytometry according to claim 1 wherein said first signaland said at least one additional signal comprises fluorescence emittedfrom a light emitting element coupled with said individual particlesafter passing through a laser beam.
 21. A flow cytometry apparatuscomprising: a fluid stream; a first signal affiliated with individualparticles in said fluid stream; at least one additional signalaffiliated with said individual particles in said fluid stream; a firstsignal detector of said first signal; at least one additional signaldetector of said at least one additional signal; a signal processorresponsive to said first signal and said at least one additional signal;a conversion of said first signal and said at least one additionalsignal into n-dimensional parameter data; a rotational alterationcalculation applied to said n-dimensional parameter data; a populationdifferentiation element of at least two populations of saidn-dimensional parameter data; a real-time classification element thatclassifies said n-dimensional parameter data based upon said rotationalalteration calculation; and a particle differentiation decision basedupon said classification of said n-dimensional parameter data.